In this directory, a notebook is provided to demonstrate how recommendation systems developed in a heterogeneous environment (e.g., Spark, GPU, etc.) can be operationalized.
| Notebook | Description |
|---|---|
| als_movie_o16n | End-to-end examples demonstrate how to build, evaluate, and deploy a Spark ALS based movie recommender with Azure services such as Databricks, Cosmos DB, and Kubernetes Services. |
| aks_locust_load_test | Load test example for a recommendation system deployed on an AKS cluster |
| lightgbm_criteo_o16n | Content-based personalization deployment of a add click prediction scenario |